White blood cells (WBCs) protect human body against different types of infections including fungal, parasitic, viral, and bacterial. The detection of abnormal regions in WBCs is a difficult task. Therefore a method is proposed for the localization of WBCs based on YOLOv2-Nucleus-Cytoplasm, which contains darkNet-19 as a basenetwork of the YOLOv2 model. In this model features are extracted from LeakyReLU-18 of darkNet-19 and supplied as an input to the YOLOv2 model. The YOLOv2-Nucleus-Cytoplasm model localizes and classifies the WBCs with maximum score labels. It also localize the WBCs into the blast and non-blast cells. After localization, the bag-of-features are extracted and optimized by using particle swarm optimization(PSO). The improved feature vector is fed to classifiers i.e., optimized naïve Bayes (O-NB) & optimized discriminant analysis (O-DA) for WBCs classification. The experiments are performed on LISC, ALL-IDB1, and ALL-IDB2 datasets.

中文翻译：

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使用 YOLOv2 和优化的特征袋识别不同类型的白细胞


白细胞 (WBC) 保护人体免受不同类型的感染，包括真菌、寄生虫、病毒和细菌。白细胞中异常区域的检测是一项艰巨的任务。因此，提出了一种基于YOLOv2-Nucleus-Cytoplasm的白细胞定位方法，其中包含darkNet-19作为YOLOv2模型的基础网络。在此模型中，从 darkNet-19 的 LeakyReLU-18 中提取特征，并将其作为输入提供给 YOLOv2 模型。 YOLOv2-Nucleus-Cytoplasm 模型对具有最大分数标签的 WBC 进行定位和分类。它还将白细胞定位到母细胞和非母细胞中。定位后，使用粒子群优化（PSO）提取和优化特征袋。改进的特征向量被馈送到分类器，即用于白细胞分类的优化朴素贝叶斯 (O-NB) 和优化判别分析 (O-DA)。实验在 LISC、ALL-IDB1 和 ALL-IDB2 数据集上进行。